






"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


UpperCAmelCase_       :							Any             =				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def 					_lowerCAmelCase(a   :    str  )      ->       Tuple:
	if "token" in model_name_or_path:
		return "rag_token"
	if "sequence" in model_name_or_path:
		return "rag_sequence"
	if "bart" in model_name_or_path:
		return "bart"
	return None






def 					_lowerCAmelCase(a   :    str			,      a   :    Dict			,      a   :    Union[str, Any]  )      ->       List[str]:
	return max(metric_fn(UpperCAmelCase_			,      UpperCAmelCase_  ) for gt in ground_truths  )






def 					_lowerCAmelCase(a   :    List[str]			,      a   :    Union[str, Any]			,      a   :    List[Any]  )      ->       List[Any]:
	_SCREAMING_SNAKE_CASE						=[line.strip() for line in open(UpperCAmelCase_			,      '''r'''  ).readlines()]
	_SCREAMING_SNAKE_CASE						=[]

	if args.gold_data_mode == "qa":
		_SCREAMING_SNAKE_CASE						=pd.read_csv(UpperCAmelCase_			,      sep='''\t'''			,      header=UpperCAmelCase_  )
		for answer_list in data[1]:
			_SCREAMING_SNAKE_CASE						=ast.literal_eval(UpperCAmelCase_  )
			answers.append(UpperCAmelCase_  )
	else:
		_SCREAMING_SNAKE_CASE						=[line.strip() for line in open(UpperCAmelCase_			,      '''r'''  ).readlines()]
		_SCREAMING_SNAKE_CASE						=[[reference] for reference in references]

	_SCREAMING_SNAKE_CASE						=_SCREAMING_SNAKE_CASE						=_SCREAMING_SNAKE_CASE						=0
	for prediction, ground_truths in zip(UpperCAmelCase_			,      UpperCAmelCase_  ):
		total += 1
		em += metric_max_over_ground_truths(UpperCAmelCase_			,      UpperCAmelCase_			,      UpperCAmelCase_  )
		fa += metric_max_over_ground_truths(UpperCAmelCase_			,      UpperCAmelCase_			,      UpperCAmelCase_  )

	_SCREAMING_SNAKE_CASE						=100.0 * em / total
	_SCREAMING_SNAKE_CASE						=100.0 * fa / total

	logger.info(f"""F1: {fa:.2f}"""  )
	logger.info(f"""EM: {em:.2f}"""  )






def 					_lowerCAmelCase(a   :    List[Any]			,      a   :    Optional[Any]			,      a   :    Any  )      ->       List[Any]:
	_SCREAMING_SNAKE_CASE						=args.k
	_SCREAMING_SNAKE_CASE						=[line.strip() for line in open(UpperCAmelCase_			,      '''r'''  ).readlines()]
	_SCREAMING_SNAKE_CASE						=[line.strip() for line in open(UpperCAmelCase_			,      '''r'''  ).readlines()]

	_SCREAMING_SNAKE_CASE						=_SCREAMING_SNAKE_CASE						=0
	for hypo, reference in zip(UpperCAmelCase_			,      UpperCAmelCase_  ):
		_SCREAMING_SNAKE_CASE						=set(hypo.split('''\t'''  )[:k]  )
		_SCREAMING_SNAKE_CASE						=set(reference.split('''\t'''  )  )
		total += 1
		em += len(hypo_provenance & ref_provenance  ) / k

	_SCREAMING_SNAKE_CASE						=100.0 * em / total
	logger.info(f"""Precision@{k}: {em: .2f}"""  )






def 					_lowerCAmelCase(a   :    Union[str, Any]			,      a   :    int			,      a   :    str  )      ->       Optional[int]:
	def strip_title(a   :    int  ):
		if title.startswith('''\"'''  ):
			_SCREAMING_SNAKE_CASE						=title[1:]
		if title.endswith('''\"'''  ):
			_SCREAMING_SNAKE_CASE						=title[:-1]
		return title

	_SCREAMING_SNAKE_CASE						=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    UpperCAmelCase_			,      return_tensors='''pt'''			,      padding=UpperCAmelCase_			,      truncation=UpperCAmelCase_			,      )['''input_ids'''].to(args.device  )

	_SCREAMING_SNAKE_CASE						=rag_model.rag.question_encoder(UpperCAmelCase_  )
	_SCREAMING_SNAKE_CASE						=question_enc_outputs[0]

	_SCREAMING_SNAKE_CASE						=rag_model.retriever(
	    UpperCAmelCase_			,      question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()			,      prefix=rag_model.rag.generator.config.prefix			,      n_docs=rag_model.config.n_docs			,      return_tensors='''pt'''			,      )
	_SCREAMING_SNAKE_CASE						=rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
	_SCREAMING_SNAKE_CASE						=[]
	for docs in all_docs:
		_SCREAMING_SNAKE_CASE						=[strip_title(UpperCAmelCase_  ) for title in docs['''title''']]
		provenance_strings.append('''\t'''.join(UpperCAmelCase_  )  )
	return provenance_strings






def 					_lowerCAmelCase(a   :    Optional[Any]			,      a   :    Optional[int]			,      a   :    Optional[int]  )      ->       Optional[int]:
	with torch.no_grad():
		_SCREAMING_SNAKE_CASE						=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    UpperCAmelCase_			,      return_tensors='''pt'''			,      padding=UpperCAmelCase_			,      truncation=UpperCAmelCase_  )

		_SCREAMING_SNAKE_CASE						=inputs_dict.input_ids.to(args.device  )
		_SCREAMING_SNAKE_CASE						=inputs_dict.attention_mask.to(args.device  )
		_SCREAMING_SNAKE_CASE						=rag_model.generate(  # rag_model overwrites generate
		    UpperCAmelCase_			,      attention_mask=UpperCAmelCase_			,      num_beams=args.num_beams			,      min_length=args.min_length			,      max_length=args.max_length			,      early_stopping=UpperCAmelCase_			,      num_return_sequences=1			,      bad_words_ids=[[0, 0]]			,      )
		_SCREAMING_SNAKE_CASE						=rag_model.retriever.generator_tokenizer.batch_decode(UpperCAmelCase_			,      skip_special_tokens=UpperCAmelCase_  )

		if args.print_predictions:
			for q, a in zip(UpperCAmelCase_			,      UpperCAmelCase_  ):
				logger.info('''Q: {} - A: {}'''.format(UpperCAmelCase_			,      UpperCAmelCase_  )  )

		return answers






def 					_lowerCAmelCase()      ->       Any:
	_SCREAMING_SNAKE_CASE						=argparse.ArgumentParser()
	parser.add_argument(
	    '''--model_type'''			,      choices=['''rag_sequence''', '''rag_token''', '''bart''']			,      type=UpperCAmelCase_			,      help=(
	        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
	        ''' model_name_or_path'''
	    )			,      )
	parser.add_argument(
	    '''--index_name'''			,      default=UpperCAmelCase_			,      choices=['''exact''', '''compressed''', '''legacy''']			,      type=UpperCAmelCase_			,      help='''RAG model retriever type'''			,      )
	parser.add_argument(
	    '''--index_path'''			,      default=UpperCAmelCase_			,      type=UpperCAmelCase_			,      help='''Path to the retrieval index'''			,      )
	parser.add_argument('''--n_docs'''			,      default=5			,      type=UpperCAmelCase_			,      help='''Number of retrieved docs'''  )
	parser.add_argument(
	    '''--model_name_or_path'''			,      default=UpperCAmelCase_			,      type=UpperCAmelCase_			,      required=UpperCAmelCase_			,      help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''			,      )
	parser.add_argument(
	    '''--eval_mode'''			,      choices=['''e2e''', '''retrieval''']			,      default='''e2e'''			,      type=UpperCAmelCase_			,      help=(
	        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
	        ''' precision@k.'''
	    )			,      )
	parser.add_argument('''--k'''			,      default=1			,      type=UpperCAmelCase_			,      help='''k for the precision@k calculation'''  )
	parser.add_argument(
	    '''--evaluation_set'''			,      default=UpperCAmelCase_			,      type=UpperCAmelCase_			,      required=UpperCAmelCase_			,      help='''Path to a file containing evaluation samples'''			,      )
	parser.add_argument(
	    '''--gold_data_path'''			,      default=UpperCAmelCase_			,      type=UpperCAmelCase_			,      required=UpperCAmelCase_			,      help='''Path to a tab-separated file with gold samples'''			,      )
	parser.add_argument(
	    '''--gold_data_mode'''			,      default='''qa'''			,      type=UpperCAmelCase_			,      choices=['''qa''', '''ans''']			,      help=(
	        '''Format of the gold data file'''
	        '''qa - a single line in the following format: question [tab] answer_list'''
	        '''ans - a single line of the gold file contains the expected answer string'''
	    )			,      )
	parser.add_argument(
	    '''--predictions_path'''			,      type=UpperCAmelCase_			,      default='''predictions.txt'''			,      help='''Name of the predictions file, to be stored in the checkpoints directory'''			,      )
	parser.add_argument(
	    '''--eval_all_checkpoints'''			,      action='''store_true'''			,      help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''			,      )
	parser.add_argument(
	    '''--eval_batch_size'''			,      default=8			,      type=UpperCAmelCase_			,      help='''Batch size per GPU/CPU for evaluation.'''			,      )
	parser.add_argument(
	    '''--recalculate'''			,      help='''Recalculate predictions even if the prediction file exists'''			,      action='''store_true'''			,      )
	parser.add_argument(
	    '''--num_beams'''			,      default=4			,      type=UpperCAmelCase_			,      help='''Number of beams to be used when generating answers'''			,      )
	parser.add_argument('''--min_length'''			,      default=1			,      type=UpperCAmelCase_			,      help='''Min length of the generated answers'''  )
	parser.add_argument('''--max_length'''			,      default=50			,      type=UpperCAmelCase_			,      help='''Max length of the generated answers'''  )

	parser.add_argument(
	    '''--print_predictions'''			,      action='''store_true'''			,      help='''If True, prints predictions while evaluating.'''			,      )
	parser.add_argument(
	    '''--print_docs'''			,      action='''store_true'''			,      help='''If True, prints docs retried while generating.'''			,      )
	_SCREAMING_SNAKE_CASE						=parser.parse_args()
	_SCREAMING_SNAKE_CASE						=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )
	return args






def 					_lowerCAmelCase(a   :    int  )      ->       List[Any]:
	_SCREAMING_SNAKE_CASE						={}
	if args.model_type is None:
		_SCREAMING_SNAKE_CASE						=infer_model_type(args.model_name_or_path  )
		assert args.model_type is not None
	if args.model_type.startswith('''rag'''  ):
		_SCREAMING_SNAKE_CASE						=RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
		_SCREAMING_SNAKE_CASE						=args.n_docs
		if args.index_name is not None:
			_SCREAMING_SNAKE_CASE						=args.index_name
		if args.index_path is not None:
			_SCREAMING_SNAKE_CASE						=args.index_path
	else:
		_SCREAMING_SNAKE_CASE						=BartForConditionalGeneration

	_SCREAMING_SNAKE_CASE						=(
	    [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
	    if args.eval_all_checkpoints
	    else [args.model_name_or_path]
	)

	logger.info('''Evaluate the following checkpoints: %s'''			,      UpperCAmelCase_  )

	_SCREAMING_SNAKE_CASE						=get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
	_SCREAMING_SNAKE_CASE						=evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

	for checkpoint in checkpoints:
		if os.path.exists(args.predictions_path  ) and (not args.recalculate):
			logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path  )  )
			score_fn(UpperCAmelCase_			,      args.predictions_path			,      args.gold_data_path  )
			continue

		logger.info('''***** Running evaluation for {} *****'''.format(UpperCAmelCase_  )  )
		logger.info('''  Batch size = %d'''			,      args.eval_batch_size  )
		logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path  )  )

		if args.model_type.startswith('''rag'''  ):
			_SCREAMING_SNAKE_CASE						=RagRetriever.from_pretrained(UpperCAmelCase_			,      **UpperCAmelCase_  )
			_SCREAMING_SNAKE_CASE						=model_class.from_pretrained(UpperCAmelCase_			,      retriever=UpperCAmelCase_			,      **UpperCAmelCase_  )
			model.retriever.init_retrieval()
		else:
			_SCREAMING_SNAKE_CASE						=model_class.from_pretrained(UpperCAmelCase_			,      **UpperCAmelCase_  )
		model.to(args.device  )

		with open(args.evaluation_set			,      '''r'''  ) as eval_file, open(args.predictions_path			,      '''w'''  ) as preds_file:
			_SCREAMING_SNAKE_CASE						=[]
			for line in tqdm(UpperCAmelCase_  ):
				questions.append(line.strip()  )
				if len(UpperCAmelCase_  ) == args.eval_batch_size:
					_SCREAMING_SNAKE_CASE						=evaluate_batch_fn(UpperCAmelCase_			,      UpperCAmelCase_			,      UpperCAmelCase_  )
					preds_file.write('''\n'''.join(UpperCAmelCase_  ) + '''\n'''  )
					preds_file.flush()
					_SCREAMING_SNAKE_CASE						=[]
			if len(UpperCAmelCase_  ) > 0:
				_SCREAMING_SNAKE_CASE						=evaluate_batch_fn(UpperCAmelCase_			,      UpperCAmelCase_			,      UpperCAmelCase_  )
				preds_file.write('''\n'''.join(UpperCAmelCase_  )  )
				preds_file.flush()

			score_fn(UpperCAmelCase_			,      args.predictions_path			,      args.gold_data_path  )


if __name__ == "__main__":
							UpperCAmelCase_       :							Optional[Any]             =				get_args()
							main(args)






"""simple docstring"""


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



@add_end_docstrings(_lowerCAmelCase						)
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   **UpperCAmelCase_			:				List[str]      )->	Tuple:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )
              requires_backends(self							,   "vision"      )
              requires_backends(self							,   "torch"      )

              if self.framework != "pt":
                     raise ValueError(f"The {self.__class__} is only available in PyTorch."      )

              self.check_model_type(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   **UpperCAmelCase_			:				int      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              # preprocess args
              if "points_per_batch" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_batch"]
              if "points_per_crop" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_crop"]
              if "crops_n_layers" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_n_layers"]
              if "crop_overlap_ratio" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_overlap_ratio"]
              if "crop_n_points_downscale_factor" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_n_points_downscale_factor"]
              # postprocess args
              if "pred_iou_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["pred_iou_thresh"]
              if "stability_score_offset" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_offset"]
              if "mask_threshold" in kwargs:
                     UpperCamelCase       			=		kwargs["mask_threshold"]
              if "stability_score_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_thresh"]
              if "crops_nms_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_nms_thresh"]
              if "output_rle_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_rle_mask"]
              if "output_bboxes_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_bboxes_mask"]
              return preprocess_kwargs, forward_params, postprocess_kwargs






       def __call__(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   *UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				Tuple=None							,   **UpperCAmelCase_			:				str      )->	Any:







              """simple docstring"""
              return super().__call__(UpperCAmelCase_							,   *UpperCAmelCase_							,   num_workers=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				str=64							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				float = 512 / 1_500							,   UpperCAmelCase_			:				Optional[int] = 32							,   UpperCAmelCase_			:				Optional[int] = 1							,   )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		load_image(UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor.size["longest_edge"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.generate_crop_boxes(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor(images=UpperCAmelCase_							,   return_tensors="pt"      )

              with self.device_placement():
                     if self.framework == "pt":
                            UpperCamelCase       			=		self.get_inference_context()
                            with inference_context():
                                   UpperCamelCase       			=		self._ensure_tensor_on_device(UpperCAmelCase_							,   device=self.device      )
                                   UpperCamelCase       			=		self.model.get_image_embeddings(model_inputs.pop("pixel_values"      )      )
                                   UpperCamelCase       			=		image_embeddings

              UpperCamelCase       			=		grid_points.shape[1]
              UpperCamelCase       			=		points_per_batch if points_per_batch is not None else n_points

              if points_per_batch <= 0:
                     raise ValueError(
                         "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
                         "To return all points at once, set points_per_batch to None"      )

              for i in range(0							,   UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		grid_points[:, i : i + points_per_batch, :, :]
                     UpperCamelCase       			=		input_labels[:, i : i + points_per_batch]
                     UpperCamelCase       			=		i == n_points - points_per_batch
                     yield {
                         "input_points": batched_points,
                         "input_labels": labels,
                         "input_boxes": crop_boxes,
                         "is_last": is_last,
                         **model_inputs,
                     }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				int=0.88							,   UpperCAmelCase_			:				Optional[int]=0.95							,   UpperCAmelCase_			:				Dict=0							,   UpperCAmelCase_			:				int=1							,   )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		model_inputs.pop("input_boxes"      )
              UpperCamelCase       			=		model_inputs.pop("is_last"      )
              UpperCamelCase       			=		model_inputs.pop("original_sizes"      ).tolist()
              UpperCamelCase       			=		model_inputs.pop("reshaped_input_sizes"      ).tolist()

              UpperCamelCase       			=		self.model(**UpperCAmelCase_      )

              # post processing happens here in order to avoid CPU GPU copies of ALL the masks
              UpperCamelCase       			=		model_outputs["pred_masks"]
              UpperCamelCase       			=		self.image_processor.post_process_masks(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   binarize=UpperCAmelCase_      )
              UpperCamelCase       			=		model_outputs["iou_scores"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.filter_masks(
                  masks[0]							,   iou_scores[0]							,   original_sizes[0]							,   input_boxes[0]							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   )
              return {
                  "masks": masks,
                  "is_last": is_last,
                  "boxes": boxes,
                  "iou_scores": iou_scores,
              }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				List[str]=False							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Optional[int]=0.7							,   )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              for model_output in model_outputs:
                     all_scores.append(model_output.pop("iou_scores"      )      )
                     all_masks.extend(model_output.pop("masks"      )      )
                     all_boxes.append(model_output.pop("boxes"      )      )

              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.post_process_for_mask_generation(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		defaultdict(UpperCAmelCase_      )
              for output in model_outputs:
                     for k, v in output.items():
                            extra[k].append(UpperCAmelCase_      )

              UpperCamelCase       			=		{}
              if output_rle_mask:
                     UpperCamelCase       			=		rle_mask

              if output_bboxes_mask:
                     UpperCamelCase       			=		bounding_boxes

              return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





